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<table width="100%" summary="page for epilepsy"><tr><td>epilepsy</td><td style="text-align: right;">R Documentation</td></tr></table>

<h2>Epilepsy Attacks Data Set</h2>

<h3>Description</h3>

<p>Data from a clinical trial of 59 patients with epilepsy
(Breslow, 1996) in order to illustrate diagnostic techniques in
Poisson regression.
</p>


<h3>Usage</h3>

<pre>data(epilepsy, package="robustbase")</pre>


<h3>Format</h3>

<p>A data frame with 59 observations on the following 11 variables.
</p>

<dl>
<dt><code>ID</code></dt><dd><p>Patient identification number</p>
</dd>
<dt><code>Y1</code></dt><dd><p>Number of epilepsy attacks patients have during the
first follow-up period</p>
</dd>
<dt><code>Y2</code></dt><dd><p>Number of epilepsy attacks patients have during the
second follow-up period</p>
</dd>
<dt><code>Y3</code></dt><dd><p>Number of epilepsy attacks patients have during the
third follow-up period</p>
</dd>
<dt><code>Y4</code></dt><dd><p>Number of epilepsy attacks patients have during the
forth follow-up period</p>
</dd>
<dt><code>Base</code></dt><dd><p>Number of epileptic attacks
recorded during 8 week period prior to randomization</p>
</dd>
<dt><code>Age</code></dt><dd><p>Age of the patients</p>
</dd>
<dt><code>Trt</code></dt><dd><p>a factor with levels <code>placebo</code>
<code>progabide</code> indicating whether the anti-epilepsy
drug Progabide has been applied or not</p>
</dd>
<dt><code>Ysum</code></dt><dd><p>Total number of epilepsy attacks patients have
during the four follow-up periods  </p>
</dd>
<dt><code>Age10</code></dt><dd><p>Age of the patients devided by 10</p>
</dd>
<dt><code>Base4</code></dt><dd><p>Variable <code>Base</code> devided by 4</p>
</dd>
</dl>



<h3>Details</h3>

<p>Thall and Vail reported data from  a clinical trial of 59 patients
with epilepsy, 31 of whom were randomized to receive the anti-epilepsy
drug Progabide and 28 of whom received a placebo. Baseline data
consisted of the patient's age and the number of epileptic seizures
recorded during 8 week period prior to randomization. The response
consisted of counts of seizures occuring during the four consecutive
follow-up periods of two weeks each.
</p>


<h3>Source</h3>

<p>Thall, P.F. and Vail S.C. (1990)
Some covariance models for longitudinal count data with overdispersion.
<em>Biometrics</em> <b>46</b>, 657&ndash;671.
</p>


<h3>References</h3>

<p>Diggle, P.J., Liang, K.Y., and Zeger, S.L. (1994)
<em>Analysis of Longitudinal Data</em>; Clarendon Press.
</p>
<p>Breslow N. E. (1996)
Generalized linear models: Checking assumptions and strengthening
conclusions.
<em>Statistica Applicata</em> <b>8</b>, 23&ndash;41.
</p>


<h3>Examples</h3>

<pre>
data(epilepsy)
str(epilepsy)
pairs(epilepsy[,c("Ysum","Base4","Trt","Age10")])

Efit1 &lt;- glm(Ysum ~ Age10 + Base4*Trt, family=poisson, data=epilepsy)
summary(Efit1)

## Robust Fit :
Efit2 &lt;- glmrob(Ysum ~ Age10 + Base4*Trt, family=poisson, data=epilepsy,
                method = "Mqle",
                tcc=1.2, maxit=100)
summary(Efit2)
</pre>


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